Interpretable Clustering of Multivariate Time Series with Time2Feat

Summary: Time2Feat extracts interpretable intra- and inter-signal features for multivariate time series and applies dimensionality reduction/feature selection to produce a compact, explainable representation for clustering. Domain experts can semi-supervise via exemplar series to steer clusters and shrink feature sets. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: 13248
Venue: VLDB
Year: 2023
Pagerank: 4.7793885e-05
Overall Rank: 7,278 | 49.37%
DOI: 10.14778/3611540.3611604

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
10,309	CLaP - State Detection from Time Series	2026	VLDB	4.1945683e-05
10,331	MS-Index: Fast Top-k Subsequence Search for Multivariate Time Series under Euclidean Distance	2026	VLDB	4.1945683e-05
10,466	A Structured Study of Multivariate Time-Series Distance Measures	2025	SIGMOD	4.1945683e-05
11,059	DARKER: Efficient Transformer with Data-driven Attention Mechanism for Time Series	2024	VLDB	4.1945683e-05

Outgoing Citations (Sorted by Pagerank)

Showing 1 of 1 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
6,851	Time2Feat: Learning Interpretable Representations for Multivariate Time Series Clustering	2023	VLDB	4.9084229e-05

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